Welcome to MSUG: Michigan SAS Users Group

Regression Expression! 24 Regression Methods in SAS

With so many regression procedures available for different situations, it can be difficult to know the breadth of available methods and how to select the ones to apply to a given problem. This course offers an overview of 24 regression-based methods. A decision flowchart is provided to assist in selecting the most useful regression procedures for a given context. The course is practical and example driven, emphasizing which procedures to consider and how to apply them in real situations. A quick introduction to each method is followed by two worked examples, with discussion of use cases, options in the SAS procedures, and producing graphical output. The course begins with a basic overview of linear regression, progressing to more advanced techniques. Course modules include basic regression, procedures for specific data issues and needs (e.g., robust regression for outliers), special model types (e.g., quantile regression), logistic regression methods, and mixed, non-linear, and non-parametric SAS procedures. This course will help discern which statistical methods should be considered in a given situation and provide details with source code and examples for using specific procedures.

Course OutlinePart 1: Regression Basics

Overview of regression theory

Simple Linear Regression

General Linear Models

Part 2: Special Data Needs

Outliers and robust regression

Ill-conditioned data and orthogonal regression

Transformation of data before modeling

Part 3: Special Model Types

Quantile regression

Partial least squares

Regression on survey data

Proportional hazards with survey data

Contingency table regression

Response surface models

Survival analysis

Proportional hazards models

Structural equation modeling

Part 4: Logistic Regression

Standard logistic regression

General logistic, including Poisson regression

Probit models

Logistic regression on survey data

Part 5: Linear Mixed Regression

Mixed models and meta-analysis

General mixed models

Part 6: Non-Linear Methods

Standard non-linear regression

Non-linear mixed models

Part 7: Non-Parametric Methods

Local polynomial regression (LOESS)

Additive models

About the Author

With a PhD in statistical astrophysics, David Corliss works as a data scientist in the automotive industry while continuing astrophysics research on the side. As an instructor, his focus in on analytic methods and best practices applied to emerging technology. He serves on the steering committee for the Conference on Statistical Practice, President of the Detroit ASA Chapter, and is the founder of Peace-Work, a volunteer cooperative of statisticians and data scientists applying statistical methods to issue-driven advocacy.

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